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    • threshold value.
  1. Binning:
    • Grouping values into bins to reduce the impact of extreme values.
  2. Imputation:
    • For multivariate data, impute missing values using regression-based methods that consider relationships with other variables.
  3. Robust Statistical Methods:
    • Use statistical techniques that are less affected by outliers, like the Median Absolute Deviation (MAD) for estimating variability.
  4. Use of Robust Models:
    • Models like Random Forests, Support Vector Machines, and robust regression models are less sensitive to outliers.

Data Visualization Techniques:

  1. Box Plots:
    • Displaying the distribution, median, and outliers of a dataset.
  2. Scatter Plots:
    • Visualizing the relationship between two variables, making outliers easy to spot.
  3. Histograms:
    • Providing a visual representation of the distribution of a single variable.
  4. QQ Plots:
    • Comparing the distribution of data against a theoretical normal distribution.
  5. Violin Plots:
    • Combining a box plot and a kernel density plot to show the distribution of the data.
  6. Heatmaps:
    • Useful for visualizing multivariate data, especially in correlation analysis.
  7. 3D Scatter Plots:
    • Useful for visualizing relationships in three-dimensional space.
  8. Interactive Visualizations:
    • Tools like Tableau, Power BI, or Plotly can create dynamic and interactive visualizations for exploring data.

Remember that the choice of visualization technique and outlier handling method should be driven by the nature of the data and the specific goals of your analysis. It’s also important to document and justify your decisions for reproducibility and transparency in your analysis.